ggplot2 brings the Grammar of Graphics to life in R, providing a layered approach to building publication-quality charts. Whether you need a simple scatter plot or a complex faceted dashboard, ggplot2 offers intuitive syntax for mapping data variables to visual aesthetics, controlling scales, and fine-tuning themes. In this post, you’ll learn: How to build scatter plots, bar charts, and histograms Techniques for mapping aesthetics like color, size, and shape Customizing labels, colors, and themes for polished visuals Faceting for multi-panel displays Adding interactivity with plotly and ggiraph Best practices for saving and optimizing plots By the end, you’ll be equipped to transform raw data into compelling visual narratives that inform, engage, and persuade. 1. Getting Started with ggplot2 Install and load ggplot2 (part of the tidyverse) before you begin: r install.packages("ggplot2") library(ggplot2) Import or convert your data into a tibble for seamless integration: r l...
Practical training for data analysts and rational investors. Guides on SQL, data analysis, ETL, and personal finance to make data-driven decisions.